Word-alignment plays a basic role in the fields of language translation, language learning, and other natural language processing and handling involving more than one language. Often a first body of text is translated into a second matching body of text of a different language from that of the first body of text. For example, the text of a newspaper article in English is translated into a matching article, but in German.
These translated bodies of text (i.e., bilingual parallel bodies) have alignments between corresponding segments within the bodies of text. Such alignment is commonly done by sentences and by words. The alignment itself is often performed, at least in part, by a machine translation process of a computer system programmed to do such language translations.
Presumably, the word-alignment data of bilingual parallel bodies would be helpful to someone trying to learn a language. With this information, a language learner can associate the meanings of words, in context, in one language with the contextual meaning of essentially the same words in another language. However, the conventional approaches of computer-assisted language learning have not yet successfully, efficiently, and elegantly depicted word-alignment data of bilingual parallel bodies in a manner that greatly helps in language learning.
Furthermore, machine translation is notoriously lacking in the nuances of the meaning of a natural language. This is especially so in terms of contextual nuances. Fluent human speakers/writers of natural languages do a better job of capturing those contextual nuances in meanings. However, contextual meanings and interpretations vary by person and over time. Furthermore, employing armies of human translators is typically beyond the time and resources available and affordable to most translation projects.